Energy Consumption Forecasting Using Ml

Energy consumption forecasting has become an essential component of modern power system management due to increasing global energy demand, rapid urbanization, and the growing complexity of electricity distribution networks. Accurate forecasting enables utilities and energy providers to optimize resource allocation, reduce operational costs, improve grid stability, and support sustainable energy planning. Traditional statistical techniques such as autoregressive integrated moving average models and exponential smoothing methods have been widely used for load prediction; however, these methods often struggle to capture nonlinear patterns and dynamic relationships present in real-world energy consumption data. In recent years, machine learning techniques have demonstrated superior capabilities in modeling complex temporal patterns and extracting hidden relationships from large datasets. This study presents a machine learning– based energy consumption forecasting system using the Random Forest Regressor algorithm. The proposed system utilizes historical energy usage data along with relevant environmental and temporal features such as temperature, humidity, time of day, day of week, and seasonal indicators to improve prediction accuracy. The system architecture includes data preprocessing, feature engineering, model training, evaluation, and prediction modules. Random Forest was selected due to its ensemble learning capability, robustness against overfitting, and ability to handle nonlinear relationships and noisy data. Experimental evaluation demonstrates that the proposed model produces reliable forecasting performance and can assist energy providers in demand planning and operational decision-making. The implementation of such intelligent forecasting systems can significantly enhance energy efficiency, support renewable energy integration, and contribute to the development of smart grid infrastructure